[논문]배전시스템 운영계획을 위한 신재생에너지원 발전량 예측 방법

조진태; 김홍주; 류호성; 조영표

doi:10.18770/kepco.2022.08.01.21

배전시스템 운영계획을 위한 신재생에너지원 발전량 예측 방법
Renewable Power Generation Forecasting Method for Distribution System: A Review 원문보기

KEPCO Journal on electric power and energy, v.8 no.1, 2022년, pp.21 - 29

조진태 (KEPCO Research Institute, Korea Electric Power Corporation) , 김홍주 (KEPCO Research Institute, Korea Electric Power Corporation) , 류호성 (KEPCO Research Institute, Korea Electric Power Corporation) , 조영표 (KEPCO Research Institute, Korea Electric Power Corporation)

Abstract ▼ AI-Helper

Power generated from renewable energy has continuously increased recently. As the distributed generation begins to interconnect in the distribution system, an accurate generation forecasting has become important in efficient distribution planning. This paper explained method and current state of distributed power generation forecasting models. This paper presented selecting input and output variables for the forecasting model. In addition, this paper analyzed input variables and forecasting models that can use as mid-to long-term distributed power generation forecasting.

주제어

표/그림 (20)

그림 Fig. 1. Distributed power generation forecasting classification over time
그림 Fig. 2. Frequency of algorithm by forecasting period
그림 Fig. 3. Frequency of input variable by forecasting period
그림 Fig. 4. Frequency of input variable by forecasting region
그림 Fig. 5. Frequency of algorithm by forecasting method
그림 Fig. 6. Flowchart of VAR model based on renewable energy and economic indicators
표 TABLE 1 Comparison of forecast accuracy
그림 Fig. 7. Flowchart of suggested GP model
그림 Fig. 8. Block diagram of ANN
그림 Fig. 9. Block diagram of RNN
그림 Fig. 10. Block diagram of LSTM
표 Table 2 Internal variable of RNN
표 Table 3 Comparison of decision parameter of each stage
그림 Fig. 11. Block diagram of GRU
그림 Fig. 12. Block diagram of bidirectional GRU
그림 Fig. 13. Block diagram of SVR
표 Table 4 Performance comparison between LSTM and Other Models
그림 Fig. 14. Accuracy of each forecasting model and ensemble model
표 Table 5 Comparison of SARIMA and Hybrid Model
표 Table 6 Performance comparison between hybrid models based on LSTM and Other Models

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